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A Hybrid Strategy of Differential Evolution and Modified Particle Swarm Optimization for Numerical Solution of a Parallel Manipulator

机译:一种差分演化和修改粒子群优化的混合策略,对并联机械手数溶液的优化

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摘要

This paper presents a hybrid strategy combined with a differential evolution (DE) algorithm and a modified particle swarm optimization (PSO), denominated as DEMPSO, to solve the nonlinear model of the forward kinematics. The proposed DEMPSO takes the best advantage of the convergence rate of MPSO and the global optimization of DE. A comparison study between the DEMPSO and the other optimization algorithms such as the DE algorithm, PSO algorithm, and MPSO algorithm is performed to obtain the numerical solution of the forward kinematics of a 3-RPS parallel manipulator. The forward kinematic model of the 3-RPS parallel manipulator has been developed and it is essentially a nonlinear algebraic equation which is dependent on the structure of the mechanism. A constraint equation based on the assembly relationship is utilized to express the position and orientation of the manipulator. Five configurations with different positions and orientations are used as an example to illustrate the effectiveness of the proposed DEMPSO for solving the kinematic problem of parallel manipulators. And the comparison study results of DEMPSO and the other optimization algorithms also show that DEMPSO can provide a better performance regarding the convergence rate and global searching properties.
机译:本文提出了一种混合策略与差分进化(DE)算法和改性粒子群优化(PSO),命名为DEMPSO组合,解决了正向运动学的非线性模型。拟议的dempso是MPSO的收敛速度和DE的全球优化的最佳优势。 DEMPSO与其他优化算法之间的比较研究,如DE算法,PSO算法和MPSO算法,以获得3 RPS并行机械手的前向运动学的数值解。已经开发了3-RPS并联机械手的前进运动模型,并且基本上是非线性代数方程,其取决于机构的结构。基于组装关系的约束方程被利用来表达操纵器的位置和方向。使用不同位置和取向的五种配置用作示例以说明所提出的DEMPSO的有效性来解决并行操纵器的运动问题。并且DEMPSO的比较研究结果和其他优化算法也表明DEMPSO可以提供关于收敛速率和全局搜索属性的更好的性能。

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